class_docstr: Planet Earth Landscape problem defined in
  Machine Learning and Data Analysis (MLDA) Problem Set v1.0, Gallagher et al., 2018, PPSN
  https://drive.google.com/file/d/1fc1sVwoLJ0LsQ5fzi4jo3rDJHQ6VGQ1h/view
  and reverted to look for the minimum value (0). This is problem P4.
  
  Parameters
  ----------
  transform: None, "gaussian" or "square"
      whether use the image [0, 4319]x[0, 2159] (None) or to use a Gaussian transform.
  
  Note
  ----
  - the initial image is 4320x2160
  - sampling outside yields a +inf value (except for Gaussian, since large values are mapped to border indices)
  - the image is actually a variant of the one proposed in the article. Indeed, this image
    has a much better z-resolution. It is not directly proportional to the altitude though
    since it is an artificial rescaling to greyscale of a color image.
code: def _get_pixel_value(self, x: float, y: float) -> float:
      z = np.round([x, y])
      if (
          np.min(z) < 0
          or z[0] >= self._image.shape[0]
          or z[1] >= self._image.shape[1]
      ):
          return float("inf")  # could propose other limit conditions
      return float(self._max - self._image[int(z[0]), int(z[1])])
  
imports: import numpy
  import pandas
  import scipy.spatial
  from nevergrad.parametrization import parameter
  import nevergrad.common.typing
  from ..base import ExperimentFunction
  from .None import datasets
label_desc: Use image node_id 2 for calling the Landscape obj's underlying member method code with example usage: obj._get_pixel_value(x, y) and returns: float, float